Gastos Casa
23 octubre, 2023
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 26/8/2023 | Comida | 71467 | Tami | Supermercado |
| 31/8/2023 | Netflix | 5940 | Tami | NA |
| 1/9/2023 | Comida | 94874 | Tami | Supermercado |
| 10/9/2023 | Comida | 85445 | Tami | Supermercado |
| 11/9/2023 | Agua | 10332 | Andrés | NA |
| 16/9/2023 | Comida | 79913 | Tami | Supermercado |
| 16/9/2023 | Enceres | 14400 | Tami | Incoludido |
| 16/9/2023 | Comida | 18580 | Andrés | Johnny Rockets |
| 16/9/2023 | Comida | 38151 | Andrés | Frutos secos |
| 16/9/2023 | Diosi | 21081 | Andrés | antiparasitario |
| 17/9/2023 | Diosi | 8000 | Andrés | arena |
| 18/9/2023 | Comida | 10000 | Andrés | empanadas (3) menos helado q me comí |
| 20/9/2023 | VTR | 21990 | Andrés | NA |
| 16/9/2023 | Comida | 27980 | Tami | Cajas Soul Bar |
| 23/9/2023 | Comida | 57639 | Tami | Supermercado |
| 24/9/2023 | Diosi | 8000 | Andrés | arena diosi 10kg |
| 30/9/2023 | Electricidad | 44407 | Andrés | NA |
| 30/9/2023 | Comida | 51726 | Tami | Supermercado |
| 6/10/2023 | Comida | 44298 | Tami | Supermercado |
| 14/10/2023 | Comida | 86673 | Tami | Supermercado |
| 10/10/2023 | Diosi | 6880 | Tami | Omega aceite petsu |
| 17/10/2023 | Diosi | 55990 | Tami | Comida Diosi |
| 17/10/2023 | Diosi | 50000 | Tami | Veterinaria |
| 20/10/2023 | Comida | 41970 | Tami | Barritas Wild Soul |
| 21/10/2023 | Diosi | 56170 | Andrés | n y d + 2 tarrito |
| 21/10/2023 | Comida | 50000 | Andrés | la providencia |
| 21/10/2023 | Comida | 76052 | Tami | Supermercado |
| 21/10/2023 | Diosi | 55990 | Andrés | Pelet SuperZoo |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
theme_bw()+ labs(x="Weeks")
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 8.2184e+08 2 7.9353 4e-04 ***
## lag_depvar 8.5553e+10 1 1652.1266 <2e-16 ***
## Residuals 3.2468e+10 627
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 1108.773 13348.90 0.0156703
## 2-0 28843.328 23275.985 34410.67 0.0000000
## 2-1 21614.490 18324.029 24904.95 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
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## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
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## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
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## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
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## 275 69999.29 2 58926.43
## 276 63044.86 2 69999.29
## 277 63285.29 2 63044.86
## 278 61395.43 2 63285.29
## 279 67969.43 2 61395.43
## 280 60792.57 2 67969.43
## 281 56859.14 2 60792.57
## 282 44899.43 2 56859.14
## 283 43064.14 2 44899.43
## 284 62790.29 2 43064.14
## 285 69120.71 2 62790.29
## 286 69589.43 2 69120.71
## 287 66633.29 2 69589.43
## 288 65588.57 2 66633.29
## 289 70168.57 2 65588.57
## 290 74644.71 2 70168.57
## 291 52891.00 2 74644.71
## 292 41560.57 2 52891.00
## 293 34704.86 2 41560.57
## 294 46520.00 2 34704.86
## 295 50231.00 2 46520.00
## 296 49216.71 2 50231.00
## 297 76914.86 2 49216.71
## 298 83720.71 2 76914.86
## 299 84485.00 2 83720.71
## 300 89765.00 2 84485.00
## 301 87702.86 2 89765.00
## 302 82013.86 2 87702.86
## 303 85982.43 2 82013.86
## 304 57248.43 2 85982.43
## 305 52968.43 2 57248.43
## 306 52601.86 2 52968.43
## 307 45493.29 2 52601.86
## 308 42298.86 2 45493.29
## 309 46423.71 2 42298.86
## 310 37898.00 2 46423.71
## 311 36435.14 2 37898.00
## 312 30209.57 2 36435.14
## 313 34541.86 2 30209.57
## 314 33604.71 2 34541.86
## 315 37990.71 2 33604.71
## 316 35683.43 2 37990.71
## 317 65201.86 2 35683.43
## 318 62730.57 2 65201.86
## 319 64589.14 2 62730.57
## 320 73744.86 2 64589.14
## 321 76477.71 2 73744.86
## 322 105647.43 2 76477.71
## 323 103790.29 2 105647.43
## 324 76122.29 2 103790.29
## 325 74746.14 2 76122.29
## 326 72865.71 2 74746.14
## 327 63652.57 2 72865.71
## 328 60358.29 2 63652.57
## 329 25957.14 2 60358.29
## 330 30178.43 2 25957.14
## 331 30681.57 2 30178.43
## 332 33337.29 2 30681.57
## 333 32582.71 2 33337.29
## 334 39184.43 2 32582.71
## 335 40415.71 2 39184.43
## 336 34975.43 2 40415.71
## 337 34076.14 2 34975.43
## 338 34221.14 2 34076.14
## 339 28862.57 2 34221.14
## 340 35729.86 2 28862.57
## 341 36489.29 2 35729.86
## 342 36785.14 2 36489.29
## 343 37787.71 2 36785.14
## 344 39832.14 2 37787.71
## 345 41917.86 2 39832.14
## 346 41633.57 2 41917.86
## 347 33557.00 2 41633.57
## 348 22759.57 2 33557.00
## 349 28877.86 2 22759.57
## 350 27574.00 2 28877.86
## 351 27104.71 2 27574.00
## 352 24376.14 2 27104.71
## 353 29732.29 2 24376.14
## 354 34030.00 2 29732.29
## 355 39139.71 2 34030.00
## 356 37066.57 2 39139.71
## 357 38509.29 2 37066.57
## 358 40957.29 2 38509.29
## 359 49423.00 2 40957.29
## 360 50053.29 2 49423.00
## 361 50284.14 2 50053.29
## 362 53103.86 2 50284.14
## 363 50223.00 2 53103.86
## 364 49587.14 2 50223.00
## 365 41167.71 2 49587.14
## 366 37958.71 2 41167.71
## 367 33582.29 2 37958.71
## 368 31039.43 2 33582.29
## 369 26526.57 2 31039.43
## 370 34869.43 2 26526.57
## 371 37487.43 2 34869.43
## 372 46514.43 2 37487.43
## 373 39613.43 2 46514.43
## 374 38980.57 2 39613.43
## 375 37306.14 2 38980.57
## 376 36771.29 2 37306.14
## 377 26317.00 2 36771.29
## 378 31580.71 2 26317.00
## 379 23626.57 2 31580.71
## 380 33035.71 2 23626.57
## 381 44864.57 2 33035.71
## 382 48946.14 2 44864.57
## 383 46969.57 2 48946.14
## 384 49249.57 2 46969.57
## 385 56370.14 2 49249.57
## 386 67228.71 2 56370.14
## 387 59457.29 2 67228.71
## 388 53124.71 2 59457.29
## 389 52814.14 2 53124.71
## 390 61262.00 2 52814.14
## 391 61861.14 2 61262.00
## 392 71784.71 2 61861.14
## 393 59313.29 2 71784.71
## 394 61107.00 2 59313.29
## 395 60603.43 2 61107.00
## 396 60012.57 2 60603.43
## 397 58280.43 2 60012.57
## 398 56862.71 2 58280.43
## 399 41704.43 2 56862.71
## 400 51533.00 2 41704.43
## 401 50388.71 2 51533.00
## 402 49205.29 2 50388.71
## 403 56533.29 2 49205.29
## 404 47996.14 2 56533.29
## 405 47207.57 2 47996.14
## 406 45292.00 2 47207.57
## 407 40343.43 2 45292.00
## 408 39004.86 2 40343.43
## 409 36788.43 2 39004.86
## 410 30027.57 2 36788.43
## 411 39040.14 2 30027.57
## 412 42390.14 2 39040.14
## 413 36291.14 2 42390.14
## 414 30668.29 2 36291.14
## 415 47693.00 2 30668.29
## 416 52094.43 2 47693.00
## 417 56592.57 2 52094.43
## 418 47971.43 2 56592.57
## 419 43762.43 2 47971.43
## 420 42246.71 2 43762.43
## 421 46352.43 2 42246.71
## 422 33094.86 2 46352.43
## 423 32784.86 2 33094.86
## 424 26212.43 2 32784.86
## 425 32611.57 2 26212.43
## 426 42144.86 2 32611.57
## 427 50034.86 2 42144.86
## 428 46332.00 2 50034.86
## 429 42976.29 2 46332.00
## 430 39456.29 2 42976.29
## 431 39328.29 2 39456.29
## 432 35296.14 2 39328.29
## 433 30875.43 2 35296.14
## 434 27709.00 2 30875.43
## 435 29513.29 2 27709.00
## 436 31630.43 2 29513.29
## 437 29346.14 2 31630.43
## 438 34916.86 2 29346.14
## 439 42020.86 2 34916.86
## 440 38303.00 2 42020.86
## 441 37966.43 2 38303.00
## 442 41408.14 2 37966.43
## 443 38988.14 2 41408.14
## 444 43555.29 2 38988.14
## 445 38114.00 2 43555.29
## 446 27847.86 2 38114.00
## 447 26517.00 2 27847.86
## 448 39518.29 2 26517.00
## 449 39153.71 2 39518.29
## 450 45623.14 2 39153.71
## 451 40627.43 2 45623.14
## 452 41027.71 2 40627.43
## 453 42882.86 2 41027.71
## 454 47139.43 2 42882.86
## 455 35547.57 2 47139.43
## 456 41099.00 2 35547.57
## 457 35859.57 2 41099.00
## 458 44524.57 2 35859.57
## 459 48554.29 2 44524.57
## 460 51554.29 2 48554.29
## 461 47810.29 2 51554.29
## 462 50490.00 2 47810.29
## 463 50720.71 2 50490.00
## 464 52720.71 2 50720.71
## 465 52145.57 2 52720.71
## 466 55515.57 2 52145.57
## 467 52457.00 2 55515.57
## 468 58239.57 2 52457.00
## 469 50523.57 2 58239.57
## 470 47788.57 2 50523.57
## 471 46170.00 2 47788.57
## 472 42305.57 2 46170.00
## 473 46605.57 2 42305.57
## 474 55149.57 2 46605.57
## 475 48769.57 2 55149.57
## 476 50719.43 2 48769.57
## 477 44753.71 2 50719.43
## 478 42898.00 2 44753.71
## 479 46141.14 2 42898.00
## 480 34022.57 2 46141.14
## 481 26651.86 2 34022.57
## 482 28791.86 2 26651.86
## 483 31879.00 2 28791.86
## 484 33584.71 2 31879.00
## 485 34690.43 2 33584.71
## 486 27410.43 2 34690.43
## 487 41755.00 2 27410.43
## 488 49379.57 2 41755.00
## 489 57198.86 2 49379.57
## 490 51144.57 2 57198.86
## 491 56677.43 2 51144.57
## 492 65416.43 2 56677.43
## 493 69779.71 2 65416.43
## 494 54046.00 2 69779.71
## 495 43259.57 2 54046.00
## 496 40998.57 2 43259.57
## 497 41368.57 2 40998.57
## 498 42274.29 2 41368.57
## 499 35962.71 2 42274.29
## 500 38709.00 2 35962.71
## 501 44778.14 2 38709.00
## 502 51282.43 2 44778.14
## 503 52094.86 2 51282.43
## 504 52221.43 2 52094.86
## 505 45011.43 2 52221.43
## 506 46545.43 2 45011.43
## 507 42263.00 2 46545.43
## 508 45417.43 2 42263.00
## 509 45034.71 2 45417.43
## 510 37840.57 2 45034.71
## 511 39135.43 2 37840.57
## 512 38191.14 2 39135.43
## 513 39456.86 2 38191.14
## 514 42479.14 2 39456.86
## 515 34282.57 2 42479.14
## 516 28878.43 2 34282.57
## 517 56227.14 2 28878.43
## 518 65569.43 2 56227.14
## 519 69751.29 2 65569.43
## 520 62171.71 2 69751.29
## 521 63705.14 2 62171.71
## 522 79257.86 2 63705.14
## 523 87244.71 2 79257.86
## 524 58568.00 2 87244.71
## 525 52695.29 2 58568.00
## 526 48911.00 2 52695.29
## 527 53924.00 2 48911.00
## 528 53358.86 2 53924.00
## 529 42121.14 2 53358.86
## 530 47835.71 2 42121.14
## 531 62329.29 2 47835.71
## 532 56056.86 2 62329.29
## 533 59946.43 2 56056.86
## 534 64511.57 2 59946.43
## 535 61137.43 2 64511.57
## 536 55448.71 2 61137.43
## 537 47964.43 2 55448.71
## 538 46425.71 2 47964.43
## 539 55512.00 2 46425.71
## 540 55226.29 2 55512.00
## 541 46709.14 2 55226.29
## 542 49254.71 2 46709.14
## 543 49056.29 2 49254.71
## 544 49850.57 2 49056.29
## 545 39145.71 2 49850.57
## 546 29799.43 2 39145.71
## 547 34769.86 2 29799.43
## 548 44061.57 2 34769.86
## 549 43829.14 2 44061.57
## 550 45782.00 2 43829.14
## 551 38924.57 2 45782.00
## 552 49242.43 2 38924.57
## 553 50565.00 2 49242.43
## 554 38864.43 2 50565.00
## 555 49786.71 2 38864.43
## 556 58787.86 2 49786.71
## 557 58060.86 2 58787.86
## 558 62179.43 2 58060.86
## 559 57333.86 2 62179.43
## 560 70797.00 2 57333.86
## 561 89901.71 2 70797.00
## 562 78558.14 2 89901.71
## 563 65466.00 2 78558.14
## 564 70525.00 2 65466.00
## 565 68377.86 2 70525.00
## 566 69736.29 2 68377.86
## 567 60085.86 2 69736.29
## 568 41757.00 2 60085.86
## 569 49780.29 2 41757.00
## 570 56540.29 2 49780.29
## 571 57894.29 2 56540.29
## 572 60270.29 2 57894.29
## 573 61011.00 2 60270.29
## 574 57721.43 2 61011.00
## 575 71741.00 2 57721.43
## 576 59576.00 2 71741.00
## 577 52390.29 2 59576.00
## 578 61092.29 2 52390.29
## 579 62814.00 2 61092.29
## 580 54908.29 2 62814.00
## 581 62082.00 2 54908.29
## 582 57017.71 2 62082.00
## 583 53634.43 2 57017.71
## 584 69169.00 2 53634.43
## 585 52488.14 2 69169.00
## 586 60895.57 2 52488.14
## 587 59856.57 2 60895.57
## 588 52670.00 2 59856.57
## 589 51874.57 2 52670.00
## 590 52190.57 2 51874.57
## 591 41562.43 2 52190.57
## 592 44764.14 2 41562.43
## 593 38612.71 2 44764.14
## 594 43473.14 2 38612.71
## 595 53505.00 2 43473.14
## 596 45870.86 2 53505.00
## 597 52578.00 2 45870.86
## 598 55300.00 2 52578.00
## 599 61789.71 2 55300.00
## 600 57391.71 2 61789.71
## 601 62902.29 2 57391.71
## 602 53250.43 2 62902.29
## 603 55402.57 2 53250.43
## 604 56291.29 2 55402.57
## 605 58933.57 2 56291.29
## 606 59590.71 2 58933.57
## 607 59065.00 2 59590.71
## 608 52399.57 2 59065.00
## 609 60483.43 2 52399.57
## 610 58262.71 2 60483.43
## 611 54939.71 2 58262.71
## 612 51169.00 2 54939.71
## 613 43113.29 2 51169.00
## 614 56289.71 2 43113.29
## 615 60739.86 2 56289.71
## 616 50363.14 2 60739.86
## 617 62270.86 2 50363.14
## 618 67061.57 2 62270.86
## 619 59609.00 2 67061.57
## 620 85054.00 2 59609.00
## 621 68023.29 2 85054.00
## 622 59242.29 2 68023.29
## 623 61535.14 2 59242.29
## 624 56215.86 2 61535.14
## 625 45152.29 2 56215.86
## 626 57409.57 2 45152.29
## 627 35151.43 2 57409.57
## 628 34991.43 2 35151.43
## 629 45944.71 2 34991.43
## 630 57944.71 2 45944.71
## 631 55706.29 2 57944.71
## 632 88593.71 2 55706.29
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 475 51077.59 14995.230
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71 63705.14 79257.86 87244.71 58568.00 52695.29 48911.00
## [526] 53924.00 53358.86 42121.14 47835.71 62329.29 56056.86 59946.43
## [533] 64511.57 61137.43 55448.71 47964.43 46425.71 55512.00 55226.29
## [540] 46709.14 49254.71 49056.29 49850.57 39145.71 29799.43 34769.86
## [547] 44061.57 43829.14 45782.00 38924.57 49242.43 50565.00 38864.43
## [554] 49786.71 58787.86 58060.86 62179.43 57333.86 70797.00 89901.71
## [561] 78558.14 65466.00 70525.00 68377.86 69736.29 60085.86 41757.00
## [568] 49780.29 56540.29 57894.29 60270.29 61011.00 57721.43 71741.00
## [575] 59576.00 52390.29 61092.29 62814.00 54908.29 62082.00 57017.71
## [582] 53634.43 69169.00 52488.14 60895.57 59856.57 52670.00 51874.57
## [589] 52190.57 41562.43 44764.14 38612.71 43473.14 53505.00 45870.86
## [596] 52578.00 55300.00 61789.71 57391.71 62902.29 53250.43 55402.57
## [603] 56291.29 58933.57 59590.71 59065.00 52399.57 60483.43 58262.71
## [610] 54939.71 51169.00 43113.29 56289.71 60739.86 50363.14 62270.86
## [617] 67061.57 59609.00 85054.00 68023.29 59242.29 61535.14 56215.86
## [624] 45152.29 57409.57 35151.43 34991.43 45944.71 57944.71 55706.29
## [631] 88593.71
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6 7
## 1921.82866 3998.78187 -496.75289 2473.89143 -2887.92585 548.44387
## 8 9 10 11 12 13
## -5612.18372 -1236.99733 -4020.44909 -523.99450 -5030.60250 -1763.93629
## 14 15 16 17 18 19
## -1053.26683 236.92575 -3350.36731 -518.06893 -2250.10471 6471.95984
## 20 21 22 23 24 25
## -1523.68893 -1220.63552 1453.13657 -1172.31899 235.81195 1708.87821
## 26 27 28 29 30 31
## -7052.22412 879.34041 8157.89356 536.36425 105.38272 -2287.49760
## 32 33 34 35 36 37
## 1643.21212 4666.86562 1296.13795 2567.51491 -1664.44168 4763.08712
## 38 39 40 41 42 43
## 4395.27024 -2121.49763 -2884.58388 -1075.42217 -10729.21997 7118.18977
## 44 45 46 47 48 49
## 2532.31346 1389.25751 8148.72900 862.75703 6695.51343 6971.86459
## 50 51 52 53 54 55
## -5542.43094 -4597.62806 -4967.61352 -7933.31056 5991.65581 -4092.41016
## 56 57 58 59 60 61
## -4976.68958 3701.36887 819.12848 -76.34544 103.74973 -5026.91421
## 62 63 64 65 66 67
## 18015.94628 3852.65078 -3398.20730 6080.82027 7581.43371 14972.02935
## 68 69 70 71 72 73
## 2234.06025 -12710.39257 -1090.79491 4810.38543 -4674.68829 -4289.78014
## 74 75 76 77 78 79
## -10471.09463 2312.62014 -5491.66006 892.74860 -6996.72577 317.31196
## 80 81 82 83 84 85
## -2545.02178 -2899.33087 -4156.21387 -799.34289 2077.59719 3595.65136
## 86 87 88 89 90 91
## 395.51602 -546.86065 134.65755 4251.85518 -1133.08823 1158.00641
## 92 93 94 95 96 97
## -2037.95906 -1055.39564 150.91936 255.24142 -7495.70993 2255.49725
## 98 99 100 101 102 103
## -8680.18963 -3153.32100 -4274.17735 -2009.21337 -1527.81119 2927.75282
## 104 105 106 107 108 109
## -2508.52819 2409.72914 -1274.67633 850.05228 2499.12001 -3186.65200
## 110 111 112 113 114 115
## -4803.88231 -1000.06282 1759.06625 11600.38365 -1125.13479 2750.89834
## 116 117 118 119 120 121
## 4380.83411 3678.83574 -885.82320 -4547.01292 -3655.18275 2317.76729
## 122 123 124 125 126 127
## -1694.23873 1345.49579 8886.24177 1022.49346 298.90949 -2371.00348
## 128 129 130 131 132 133
## 2744.50163 7176.50099 1241.10831 -8281.60166 1796.33401 4207.14748
## 134 135 136 137 138 139
## -3030.48342 -1355.89894 -821.43615 -3865.21916 1131.06529 -520.03248
## 140 141 142 143 144 145
## -2942.63691 1644.25431 -1915.75002 -7890.66010 1854.02438 -3606.41265
## 146 147 148 149 150 151
## 1933.33989 -368.70608 922.20165 -429.34993 1285.62678 1152.09564
## 152 153 154 155 156 157
## 3347.22675 -4812.37303 -1212.88584 -3288.50978 5856.62517 9760.82006
## 158 159 160 161 162 163
## -3594.57837 -4973.17467 3358.34827 46.50804 2575.16350 -5965.70106
## 164 165 166 167 168 169
## -6890.33768 3923.15851 17263.24397 3805.12107 -180.12924 -2258.27009
## 170 171 172 173 174 175
## -977.10363 3686.75076 -81.28116 -7947.47144 2845.14020 4359.92254
## 176 177 178 179 180 181
## 730.30554 8855.37836 -9009.39843 -3412.62564 -10747.27905 -11416.87747
## 182 183 184 185 186 187
## 897.96233 9022.51513 -1510.43089 5837.43561 6574.95541 13283.42904
## 188 189 190 191 192 193
## 8754.10894 -3645.56552 2760.57721 10664.41471 -1219.95227 -2104.23571
## 194 195 196 197 198 199
## -10025.54797 -6308.97437 1177.13300 -5262.77793 -9906.31206 5129.66824
## 200 201 202 203 204 205
## -3201.33418 -1877.06548 -974.38557 6333.70649 9845.35302 702.16075
## 206 207 208 209 210 211
## 3039.89920 3243.51313 5959.24781 13077.15657 -5271.29968 -11025.83674
## 212 213 214 215 216 217
## -5612.23379 -10630.87976 -5282.00988 1266.36216 -13211.88496 16007.03946
## 218 219 220 221 222 223
## 7738.47861 1590.15108 26765.64418 13015.38832 7956.89213 14681.98304
## 224 225 226 227 228 229
## -3122.87003 -1117.01470 4291.18455 866.39752 3192.48727 9437.52469
## 230 231 232 233 234 235
## 6352.83858 -1355.19363 -1376.95637 9790.38718 -11039.67867 -7058.89614
## 236 237 238 239 240 241
## -8462.01947 -10169.06826 2857.46817 1210.03595 -8398.66197 -9209.78109
## 242 243 244 245 246 247
## 8760.31598 -7911.16453 2237.17557 -10478.95099 -4372.24635 1081.42058
## 248 249 250 251 252 253
## 728.63273 -12538.95216 3253.57854 1780.68547 3998.55669 2014.87441
## 254 255 256 257 258 259
## -1232.29533 11057.13391 20982.67973 3608.49076 -3867.52769 4390.12464
## 260 261 262 263 264 265
## -1389.50255 3972.83743 -4591.35222 -10746.02603 -4773.40173 -637.04211
## 266 267 268 269 270 271
## -5299.15868 8597.92026 -4304.07480 4098.40439 -2125.00149 4378.39497
## 272 273 274 275 276 277
## 727.69835 7325.62994 -1284.82954 12108.53481 -4333.81449 1865.60607
## 278 279 280 281 282 283
## -230.26540 7963.08311 -4846.79088 -2630.63667 -11219.94186 -2807.39185
## 284 285 286 287 288 289
## 18491.33928 7919.16746 2963.57222 -394.19453 1094.10039 6569.27733
## 290 291 292 293 294 295
## 7120.98801 -18468.16713 -11158.64452 -8305.73468 9383.81555 2970.85772
## 296 297 298 299 300 301
## -1223.24629 27344.00038 10416.34592 5348.94500 9974.05691 3387.67783
## 302 303 304 305 306 307
## -534.34842 8308.91617 -23825.60554 -3484.50635 -183.70448 -6978.17444
## 308 309 310 311 312 313
## -4081.53173 2780.51281 -9279.63862 -3437.12731 -8409.23069 1257.51634
## 314 315 316 317 318 319
## -3391.80151 1797.20146 -4268.28503 27227.17112 -537.36154 3438.76299
## 320 321 322 323 324 325
## 11001.93633 5889.60138 32717.63171 5866.04225 -20210.64089 2120.89956
## 326 327 328 329 330 331
## 1419.63678 -6182.23656 -1582.12177 -33160.51335 537.83614 -2576.08421
## 332 333 334 335 336 337
## -351.49439 -3381.64892 3866.62965 -558.84883 -7054.17762 -3291.88422
## 338 339 340 341 342 343
## -2376.31963 -7859.13619 3599.71070 -1525.18323 -1880.05227 -1130.98986
## 344 345 346 347 348 349
## 54.37243 388.29168 -1683.16527 -9516.14282 -13393.05680 1977.14511
## 350 351 352 353 354 355
## -4569.24421 -3921.30308 -6247.76098 1446.39356 1154.62763 2581.78990
## 356 357 358 359 360 361
## -3869.67767 -650.56414 561.22748 6929.34134 305.67038 -3.54094
## 362 363 364 365 366 367
## 2618.36042 -2678.60534 -845.96282 -8720.54898 -4715.25282 -6342.00827
## 368 369 370 371 372 373
## -5134.86605 -7468.84319 4740.91391 210.22897 6993.96184 -7641.93976
## 374 375 376 377 378 379
## -2361.58588 -3493.74263 -2593.84401 -12589.83047 1631.77361 -10832.65085
## 380 381 382 383 384 385
## 5392.10210 9158.61360 3104.47594 -2369.44271 1604.20806 6771.13203
## 386 387 388 389 390 391
## 11528.34982 -5547.38599 -5220.90842 -105.33420 8608.64009 1969.12739
## 392 393 394 395 396 397
## 11379.31552 -9595.25346 2884.76555 844.22686 684.86140 -540.99788
## 398 399 400 401 402 403
## -474.50317 -14418.00208 8399.14233 -1166.88182 -1369.81439 6972.22167
## 404 405 406 407 408 409
## -7844.01267 -1317.42293 -2557.29674 -5864.48604 -2962.81010 -4032.26644
## 410 411 412 413 414 415
## -8893.94812 5911.75111 1539.21282 -7430.27796 -7827.12815 14015.60394
## 416 417 418 419 420 421
## 3829.18609 4555.90800 -7919.52669 -4741.38903 -2650.56728 2753.90638
## 422 423 424 425 426 427
## -14021.69939 -2971.77800 -9278.57907 2752.23413 6802.33160 6523.61256
## 428 429 430 431 432 433
## -3939.89304 -4122.76633 -4767.37919 -1879.22170 -5801.68611 -6767.40668
## 434 435 436 437 438 439
## -6145.88910 -1628.40815 -1057.29082 -5155.67775 2372.35627 4703.01775
## 440 441 442 443 444 445
## -5101.99358 -2252.87108 1477.23878 -3891.83857 2748.91255 -6605.78853
## 446 447 448 449 450 451
## -12209.49542 -4743.67540 9397.97246 -2106.91863 4674.89768 -5864.23007
## 452 453 454 455 456 457
## -1183.30202 328.85085 2995.81915 -12243.33622 3240.72492 -6755.51695
## 458 459 460 461 462 463
## 6398.95512 3003.95219 2551.03944 -3763.54933 2124.25974 58.82621
## 464 465 466 467 468 469
## 1861.13569 -427.73301 3435.08558 -2511.11389 5892.23399 -6778.63706
## 470 471 472 473 474 475
## -2902.08276 -2177.13410 -4654.66883 2956.61673 7816.10617 -5884.93063
## 476 477 478 479 480 481
## 1531.71195 -6104.76263 -2848.67754 1984.55808 -12912.94221 -9899.70198
## 482 483 484 485 486 487
## -1444.01020 -190.55400 -1130.09796 -1485.94700 -9713.39257 10869.14093
## 488 489 490 491 492 493
## 6202.38097 7488.45414 -5265.88758 5454.66251 9452.76237 6327.92302
## 494 495 496 497 498 499
## -13144.52901 -10449.32120 -3467.83050 -1160.46343 -571.78843 -7659.43284
## 500 501 502 503 504 505
## 495.00440 4210.95686 5514.81909 753.96641 184.39792 -7134.05645
## 506 507 508 509 510 511
## 577.92522 -5018.93108 1804.95175 -1280.67542 -8146.88459 -687.63318
## 512 513 514 515 516 517
## -2741.43397 -666.59627 1271.14580 -9515.11019 -7895.91491 24083.40901
## 518 519 520 521 522 523
## 9991.59551 6168.39442 -4994.45534 3033.62695 17272.40315 11932.71609
## 524 525 526 527 528 529
## -23587.63994 -4888.34104 -3640.51564 4615.09848 -245.49820 -10998.96253
## 530 531 532 533 534 535
## 4344.78959 13941.75665 -4749.67588 4514.50702 5746.82034 -1539.02416
## 536 537 538 539 540 541
## -4336.56054 -6946.39791 -2072.10528 8332.64766 261.23205 -8011.09283
## 542 543 544 545 546 547
## 1832.50251 -547.13184 417.17996 -10968.27116 -11141.96170 1836.95255
## 548 549 550 551 552 553
## 6869.69089 -1324.46313 827.55344 -7703.20601 8490.52741 972.10962
## 554 555 556 557 558 559
## -11861.72422 9086.34731 8728.58852 288.84295 5030.35373 -3344.26885
## 560 561 562 563 564 565
## 14270.86450 21839.51086 -5874.18186 -9246.43896 7030.73281 548.72044
## 566 567 568 569 570 571
## 3746.95611 -7067.45954 -17127.22226 6601.38153 6486.52550 2048.13215
## 572 573 574 575 576 577
## 3263.93975 1968.74773 -1955.51430 14882.76891 -9295.08202 -6057.05887
## 578 579 580 581 582 583
## 8802.11327 3067.40641 -6313.58101 7634.24672 -3576.92878 -2620.81584
## 584 585 586 587 588 589
## 15812.76767 -14179.08773 8521.54883 278.53502 -6017.75583 -655.27782
## 590 591 592 593 594 595
## 342.29543 -10556.61611 1751.95972 -7142.89911 2988.46052 8855.59664
## 596 597 598 599 600 601
## -7374.47264 5874.08417 2848.98214 7006.31556 -2952.47998 6326.57457
## 602 603 604 605 606 607
## -8047.08690 2375.37446 1419.99733 3300.77673 1693.84293 605.04729
## 608 609 610 611 612 613
## -5609.91620 8185.29954 -962.17219 -2382.32446 -3305.68326 -8130.41228
## 614 615 616 617 618 619
## 11948.65916 5108.40894 -9081.46775 11717.66825 6305.10368 -5252.45318
## 620 621 622 623 624 625
## 26578.37894 -12255.21238 -6443.22478 3373.74568 -3910.20431 -10415.87704
## 626 627 628 629 630 631
## 11321.37281 -21439.58369 -2527.40638 8562.97740 11177.51301 -1343.27062
## 632
## 33462.18439
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17347.46 20140.22 24312.90 24036.25 26344.64 23728.27 24430.90 19754.14
## 10 11 12 13 14 15 16 17
## 19495.73 16889.28 17651.89 14443.79 14493.98 15145.93 16810.08 15162.21
## 18 19 20 21 22 23 24 25
## 16177.10 15562.61 22509.69 21611.21 21101.01 22954.89 22293.76 22933.84
## 26 27 28 29 30 31 32 33
## 24744.51 18788.95 20482.11 28169.64 28226.19 27905.35 25580.07 26955.71
## 34 35 36 37 38 39 40 41
## 30725.29 31067.06 32449.30 30007.48 34047.73 37194.50 34306.87 31178.71
## 42 43 44 45 46 47 48 49
## 30048.51 20808.10 28183.12 30573.03 31641.41 38348.81 37853.06 42426.14
## 50 51 52 53 54 55 56 57
## 46581.43 39418.91 34091.18 29209.02 22484.49 28654.27 25300.26 21668.63
## 58 59 60 61 62 63 64 65
## 25992.73 27228.20 27519.54 27923.49 23873.34 40147.49 41956.21 37293.04
## 66 67 68 69 70 71 72 73
## 41419.57 46241.26 56705.51 54757.25 40282.51 37836.04 40796.26 35205.35
## 74 75 76 77 78 79 80 81
## 30744.52 21625.67 24765.95 20769.54 22815.73 17808.83 19785.74 19027.05
## 82 83 84 85 86 87 88 89
## 18073.36 16179.20 17432.55 20971.63 25304.91 26275.86 26300.34 26905.29
## 90 91 92 93 94 95 96 97
## 30951.52 29804.42 30784.67 28886.11 28101.22 28462.33 28861.14 22561.36
## 98 99 100 101 102 103 104 105
## 25518.76 18682.46 17560.46 15638.64 15932.67 16597.10 20984.24 20085.27
## 106 107 108 109 110 111 112 113
## 23529.25 23323.23 24967.31 27789.08 25335.03 21846.49 22116.65 24712.33
## 114 115 116 117 118 119 120 121
## 35369.13 33596.53 35398.88 38339.88 40258.39 37991.01 32911.04 29322.38
## 122 123 124 125 126 127 128 129
## 31365.38 29678.22 30837.19 38291.65 37940.95 37020.43 33943.93 35691.07
## 130 131 132 133 134 135 136 137
## 40985.75 40436.74 31806.67 33047.28 36176.05 32655.33 31073.44 30175.93
## 138 139 140 141 142 143 144 145
## 26798.79 28186.18 27960.21 25690.75 27676.46 26327.52 20051.98 23024.56
## 146 147 148 149 150 151 152 153
## 20892.80 23812.99 24342.66 25902.64 26081.23 27703.76 28979.63 31953.80
## 154 155 156 157 158 159 160 161
## 27510.60 26787.65 24389.66 30171.04 41615.01 39977.17 37392.51 42316.78
## 162 163 164 165 166 167 168 169
## 43698.41 47048.99 42601.62 37998.56 43320.04 59310.45 61480.27 59924.70
## 170 171 172 173 174 175 176 177
## 56811.10 55240.96 57891.85 56934.61 49374.15 52143.65 55814.69 55850.19
## 178 179 180 181 182 183 184 185
## 62842.68 53526.63 50339.71 41324.16 33025.32 36466.48 46376.72 45843.14
## 186 187 188 189 190 191 192 193
## 51682.04 57317.14 67893.89 73075.71 66890.99 67080.73 74015.81 69774.95
## 194 195 196 197 198 199 200 201
## 65383.41 54832.97 48977.30 50374.35 46053.31 38371.90 44673.76 42935.07
## 202 203 204 205 206 207 208 209
## 42579.96 43049.15 49713.22 58432.41 58069.10 59760.92 61385.04 65103.70
## 210 211 212 213 214 215 216 217
## 74389.16 66623.41 55038.38 49750.31 40918.87 37934.78 40988.88 31199.96
## 218 219 220 221 222 223 224 225
## 47848.81 55029.56 55914.21 78244.18 85595.82 87560.73 95006.87 86130.87
## 226 227 228 229 230 231 232 233
## 80244.10 79834.03 76548.08 75725.62 80372.02 81710.19 76252.10 71556.61
## 234 235 236 237 238 239 240 241
## 77102.11 64005.32 56194.16 48298.78 40070.82 44182.54 46294.09 39870.07
## 242 243 244 245 246 247 248 249
## 33670.54 43756.31 38113.25 41973.67 34385.53 33116.15 36701.51 39471.38
## 250 251 252 253 254 255 256 257
## 30476.28 36300.74 40029.44 45124.84 47791.15 47293.44 57397.32 74559.79
## 258 259 260 261 262 263 264 265
## 74378.38 67817.02 69270.50 65563.59 66982.07 60859.17 50338.97 46442.33
## 266 267 268 269 270 271 272 273
## 46647.73 42828.94 51464.65 47809.02 51876.43 50029.03 54018.59 54308.94
## 274 275 276 277 278 279 280 281
## 60211.26 57890.75 67378.67 61419.68 61625.69 60006.35 65639.36 59489.78
## 282 283 284 285 286 287 288 289
## 56119.37 45871.53 44298.95 61201.55 66625.86 67027.48 64494.47 63599.29
## 290 291 292 293 294 295 296 297
## 67523.73 71359.17 52719.22 43010.59 37136.18 47260.14 50439.96 49570.86
## 298 299 300 301 302 303 304 305
## 73304.37 79136.06 79790.94 84315.18 82548.21 77673.51 81074.03 56452.93
## 306 307 308 309 310 311 312 313
## 52785.56 52471.46 46380.39 43643.20 47177.64 39872.27 38618.80 33284.34
## 314 315 316 317 318 319 320 321
## 36996.52 36193.51 39951.71 37974.69 63267.93 61150.38 62742.92 70588.11
## 322 323 324 325 326 327 328 329
## 72929.80 97924.24 96332.93 72625.24 71446.08 69834.81 61940.41 59117.66
## 330 331 332 333 334 335 336 337
## 29640.59 33257.66 33688.78 35964.36 35317.80 40974.56 42029.61 37368.03
## 338 339 340 341 342 343 344 345
## 36597.46 36721.71 32130.15 38014.47 38665.20 38918.70 39777.77 41529.57
## 346 347 348 349 350 351 352 353
## 43316.74 43073.14 36152.63 26900.71 32143.24 31026.02 30623.90 28285.89
## 354 355 356 357 358 359 360 361
## 32875.37 36557.92 40936.25 39159.85 40396.06 42493.66 49747.62 50287.68
## 362 363 364 365 366 367 368 369
## 50485.50 52901.61 50433.11 49888.26 42673.97 39924.29 36174.29 33995.41
## 370 371 372 373 374 375 376 377
## 30128.51 37277.20 39520.47 47255.37 41342.16 40799.89 39365.13 38906.83
## 378 379 380 381 382 383 384 385
## 29948.94 34459.22 27643.61 35705.96 45841.67 49339.01 47645.36 49599.01
## 386 387 388 389 390 391 392 393
## 55700.36 65004.67 58345.62 52919.48 52653.36 59892.02 60405.40 68908.54
## 394 395 396 397 398 399 400 401
## 58222.23 59759.20 59327.71 58821.43 57337.22 56122.43 43133.86 51555.60
## 402 403 404 405 406 407 408 409
## 50575.10 49561.06 55840.16 48524.99 47849.30 46207.91 41967.67 40820.70
## 410 411 412 413 414 415 416 417
## 38921.52 33128.39 40850.93 43721.42 38495.41 33677.40 48265.24 52036.66
## 418 419 420 421 422 423 424 425
## 55890.96 48503.82 44897.28 43598.52 47116.56 35756.64 35491.01 29859.34
## 426 427 428 429 430 431 432 433
## 35342.53 43511.24 50271.89 47099.05 44223.66 41207.51 41097.83 37642.84
## 434 435 436 437 438 439 440 441
## 33854.89 31141.69 32687.72 34501.82 32544.50 37317.84 43404.99 40219.30
## 442 443 444 445 446 447 448 449
## 39930.90 42879.98 40806.37 44719.79 40057.35 31260.68 30120.31 41260.63
## 450 451 452 453 454 455 456 457
## 40948.25 46491.66 42211.02 42554.01 44143.61 47790.91 37858.28 42615.09
## 458 459 460 461 462 463 464 465
## 38125.62 45550.33 49003.25 51573.84 48365.74 50661.89 50859.58 52573.30
## 466 467 468 469 470 471 472 473
## 52080.49 54968.11 52347.34 57302.21 50690.65 48347.13 46960.24 43648.95
## 474 475 476 477 478 479 480 481
## 47333.47 54654.50 49187.72 50858.48 45746.68 44156.58 46935.51 36551.56
## 482 483 484 485 486 487 488 489
## 30235.87 32069.55 34714.81 36176.38 37123.82 30885.86 43177.19 49710.40
## 490 491 492 493 494 495 496 497
## 56410.46 51222.77 55963.67 63451.79 67190.53 53708.89 44466.40 42529.03
## 498 499 500 501 502 503 504 505
## 42846.07 43622.15 38214.00 40567.19 45767.61 51340.89 52037.03 52145.49
## 506 507 508 509 510 511 512 513
## 45967.50 47281.93 43612.48 46315.39 45987.46 39823.06 40932.58 40123.45
## 514 515 516 517 518 519 520 521
## 41208.00 43797.68 36774.34 32143.73 55577.83 63582.89 67166.17 60671.52
## 522 523 524 525 526 527 528 529
## 61985.45 75312.00 82155.64 57583.63 52551.52 49308.90 53604.36 53120.11
## 530 531 532 533 534 535 536 537
## 43490.92 48387.53 60806.53 55431.92 58764.75 62676.45 59785.27 54910.83
## 538 539 540 541 542 543 544 545
## 48497.82 47179.35 54965.05 54720.24 47422.21 49603.42 49433.39 50113.99
## 546 547 548 549 550 551 552 553
## 40941.39 32932.90 37191.88 45153.61 44954.45 46627.78 40751.90 49592.89
## 554 555 556 557 558 559 560 561
## 50726.15 40700.37 50059.27 57772.01 57149.07 60678.13 56526.14 68062.20
## 562 563 564 565 566 567 568 569
## 84432.32 74712.44 63494.27 67829.14 65989.33 67153.32 58884.22 43178.90
## 570 571 572 573 574 575 576 577
## 50053.76 55846.15 57006.35 59042.25 59676.94 56858.23 68871.08 58447.34
## 578 579 580 581 582 583 584 585
## 52290.17 59746.59 61221.87 54447.75 60594.64 56255.24 53356.23 66667.23
## 586 587 588 589 590 591 592 593
## 52374.02 59578.04 58687.76 52529.85 51848.28 52119.04 43012.18 45755.61
## 594 595 596 597 598 599 600 601
## 40484.68 44649.40 53245.33 46703.92 52451.02 54783.40 60344.19 56575.71
## 602 603 604 605 606 607 608 609
## 61297.52 53027.20 54871.29 55632.79 57896.87 58459.95 58009.49 52298.13
## 610 611 612 613 614 615 616 617
## 59224.89 57322.04 54474.68 51243.70 44341.06 55631.45 59444.61 50553.19
## 618 619 620 621 622 623 624 625
## 60756.47 64861.45 58475.62 80278.50 65685.51 58161.40 60126.06 55568.16
## 626 627 628 629 630 631 632
## 46088.20 56591.01 37518.83 37381.74 46767.20 57049.56 55131.53
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8219
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 7.935292 0.5313991 3.538766
## t2* 1652.126567 21.6068136 204.709903
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 3.388623 8.069928 15.00081
## 2 lag_depvar 1356.224259 1661.516045 2029.44854Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
gasto=="Chromecast"~"electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"electrodomésticos/mantención casa",
gasto=="Sopapo"~"electrodomésticos/mantención casa",
gasto=="filtro agua"~"electrodomésticos/mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
gasto=="Aspiradora"~"electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
gasto=="Pila estufa"~"electrodomésticos/mantención casa",
gasto=="Reloj"~"electrodomésticos/mantención casa",
gasto=="Arreglo"~"electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
sjPlot::theme_sjplot2() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03"))))
# scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start =
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon Oct 23 01:06:03 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Oct 23 01:06:12 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Oct 23 01:06:21 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Oct 23 01:06:31 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Oct 23 01:06:40 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Oct 23 01:06:49 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Oct 23 01:06:58 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Oct 23 01:07:07 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 16000 Mon Oct 23 01:07:16 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon Oct 23 01:07:26 2023
## =-=-=-=-=#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/ Mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/ Mantención casa",
gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Otros",
gasto=="Uber Reñaca"~"Otros",
gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021|2022",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("202",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_23 %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2023","2022","2021","2020"))| Item | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|
| Agua | 5.251556 | 5.410333 | 5.629750 | 6.7029111 |
| Comida | 365.239889 | 310.278417 | 314.087500 | 345.3436222 |
| Comunicaciones | 0.000000 | 0.000000 | 0.000000 | 0.0000000 |
| Electricidad | 35.509667 | 47.072333 | 38.297667 | 33.0219111 |
| Enceres | 22.524111 | 20.086417 | 17.443792 | 24.2113778 |
| Farmacia | 2.220000 | 1.831667 | 7.913875 | 8.4078667 |
| Gas/Bencina | 38.292444 | 44.325000 | 28.954333 | 27.7030222 |
| Diosi | 15.216778 | 31.180667 | 41.934250 | 35.3073111 |
| donaciones/regalos | 0.000000 | 0.000000 | 7.170083 | 6.1048667 |
| Electrodomésticos/ Mantención casa | 0.000000 | 3.944000 | 30.269500 | 18.4326222 |
| VTR | 14.661111 | 25.156667 | 22.121792 | 19.8275111 |
| Netflix | 5.148889 | 7.151583 | 7.090167 | 7.0104667 |
| Otros | 0.000000 | 3.151083 | 1.575542 | 0.8402889 |
| Total | 504.064444 | 499.588167 | 522.488250 | 532.9137778 |
## Joining with `by = join_by(word)`
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
tryCatch(uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf23b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf23 <-uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf23 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
## = T)`.
## Caused by warning:
## ! 41 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:2139, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)La proyección de la UF a 298 días más 2023-11-09 00:04:58 sería de: 37.238 pesos// Percentil 95% más alto proyectado: 40.466,95
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 36505.90 | 36499.36 |
| Lo.80 | 36554.68 | 36555.03 |
| Point.Forecast | 37238.50 | 38908.42 |
| Hi.80 | 39046.10 | 43642.85 |
| Hi.95 | 40038.24 | 46149.10 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(1,0,0) with non-zero mean
##
## Coefficients:
## ar1 mean
## 0.2767 1008.8581
## s.e. 0.1336 29.9259
##
## sigma^2 = 27409: log likelihood = -364.6
## AIC=735.21 AICc=735.67 BIC=741.28paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(1,0,0) errors
##
## Coefficients:
## ar1 intercept xreg
## 0.2540 727.8517 9.0752
## s.e. 0.1354 300.6833 9.6544
##
## sigma^2 = 27513: log likelihood = -364.18
## AIC=736.36 AICc=737.14 BIC=744.46forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 755.1648 | 671.1940 | 712.1943 |
| Lo.80 | 871.5073 | 788.0714 | 795.6624 |
| Point.Forecast | 1091.2836 | 1008.8581 | 980.4709 |
| Hi.80 | 1311.0598 | 1229.6447 | 1265.5960 |
| Hi.95 | 1427.4024 | 1346.5222 | 1448.1455 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 66 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Tami | Andrés |
|---|---|---|---|
| 1 | marzo_2019 | 175533 | 68268 |
| 2 | abril_2019 | 152640 | 55031 |
| 3 | mayo_2019 | 152985 | 192219 |
| 4 | junio_2019 | 291067 | 84961 |
| 5 | julio_2019 | 241389 | 205893 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
sjPlot::theme_sjplot2() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] CausalImpact_1.3.0 bsts_0.9.9 BoomSpikeSlab_1.2.5
## [4] Boom_0.9.11 scales_1.2.1 ggiraph_0.8.7
## [7] tidytext_0.4.1 DT_0.30 autoplotly_0.1.4
## [10] rvest_1.0.3 plotly_4.10.3 xts_0.13.1
## [13] forecast_8.21.1 wordcloud_2.6 RColorBrewer_1.1-3
## [16] SnowballC_0.7.1 tm_0.7-11 NLP_0.2-1
## [19] tsibble_1.1.3 lubridate_1.9.3 forcats_1.0.0
## [22] dplyr_1.1.3 purrr_1.0.1 tidyr_1.3.0
## [25] tibble_3.2.1 ggplot2_3.4.4 tidyverse_2.0.0
## [28] sjPlot_2.8.15 lattice_0.20-45 gridExtra_2.3
## [31] plotrix_3.8-2 sparklyr_1.8.3 httr_1.4.7
## [34] readxl_1.4.3 zoo_1.8-12 stringr_1.5.0
## [37] stringi_1.7.12 data.table_1.14.8 reshape2_1.4.4
## [40] fUnitRoots_4021.80 plyr_1.8.9 readr_2.1.4
##
## loaded via a namespace (and not attached):
## [1] uuid_1.1-0 backports_1.4.1 systemfonts_1.0.4
## [4] selectr_0.4-2 lazyeval_0.2.2 splines_4.1.2
## [7] crosstalk_1.2.0 digest_0.6.31 htmltools_0.5.5
## [10] fansi_1.0.4 ggfortify_0.4.16 magrittr_2.0.3
## [13] tzdb_0.4.0 modelr_0.1.11 vroom_1.6.4
## [16] timechange_0.2.0 anytime_0.3.9 tseries_0.10-54
## [19] colorspace_2.1-0 xfun_0.39 crayon_1.5.2
## [22] jsonlite_1.8.4 lme4_1.1-34 glue_1.6.2
## [25] gtable_0.3.4 emmeans_1.8.9 sjstats_0.18.2
## [28] sjmisc_2.8.9 car_3.1-2 quantmod_0.4.25
## [31] abind_1.4-5 mvtnorm_1.2-3 DBI_1.1.3
## [34] ggeffects_1.3.2 Rcpp_1.0.10 viridisLite_0.4.2
## [37] xtable_1.8-4 performance_0.10.5 bit_4.0.5
## [40] htmlwidgets_1.6.2 timeSeries_4031.107 gplots_3.1.3
## [43] ellipsis_0.3.2 spatial_7.3-14 pkgconfig_2.0.3
## [46] farver_2.1.1 nnet_7.3-16 sass_0.4.5
## [49] dbplyr_2.3.4 janitor_2.2.0 utf8_1.2.3
## [52] tidyselect_1.2.0 labeling_0.4.3 rlang_1.1.0
## [55] munsell_0.5.0 cellranger_1.1.0 tools_4.1.2
## [58] cachem_1.0.7 cli_3.6.1 generics_0.1.3
## [61] sjlabelled_1.2.0 broom_1.0.5 evaluate_0.20
## [64] fastmap_1.1.1 yaml_2.3.7 knitr_1.44
## [67] bit64_4.0.5 caTools_1.18.2 nlme_3.1-153
## [70] slam_0.1-50 xml2_1.3.3 tokenizers_0.3.0
## [73] compiler_4.1.2 rstudioapi_0.14 curl_5.1.0
## [76] bslib_0.4.2 fBasics_4031.95 Matrix_1.6-1.1
## [79] its.analysis_1.6.0 nloptr_2.0.3 urca_1.3-3
## [82] vctrs_0.6.1 pillar_1.9.0 lifecycle_1.0.3
## [85] lmtest_0.9-40 jquerylib_0.1.4 estimability_1.4.1
## [88] bitops_1.0-7 insight_0.19.6 R6_2.5.1
## [91] KernSmooth_2.23-20 janeaustenr_1.0.0 codetools_0.2-18
## [94] assertthat_0.2.1 boot_1.3-28 MASS_7.3-54
## [97] gtools_3.9.4 withr_2.5.1 fracdiff_1.5-2
## [100] bayestestR_0.13.1 parallel_4.1.2 hms_1.1.3
## [103] quadprog_1.5-8 timeDate_4022.108 minqa_1.2.6
## [106] snakecase_0.11.1 rmarkdown_2.25 carData_3.0-5
## [109] TTR_0.24.3 base64enc_0.1-3#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))